Adaptive learning-driven high-throughput synthesis of oxygen reduction reaction Fe–N–C electrocatalysts-Reference-Cited by-同舟云学术

Adaptive learning-driven high-throughput synthesis of oxygen reduction reaction Fe–N–C electrocatalysts

Published:2023-03 Issue: Volume:559 Page:232583
ISSN:0378-7753
Container-title:Journal of Power Sources
language:en
Short-container-title:Journal of Power Sources

Author:

Kort-Kamp Wilton J.M.^ORCID,Ferrandon Magali,Wang Xiaoping,Park Jae Hyung,Malla Rajesh K.,Ahmed Towfiq,Holby Edward F.,Myers Deborah J.,Zelenay Piotr

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Physical and Theoretical Chemistry,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference72 articles.

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2. Confined Pt and M (M = Fe, Co) nanoparticles on NC with ultralow Pt loading for oxygen reduction reaction;Journal of Electroanalytical Chemistry;2024-08

3. Activity versus stability of atomically dispersed transition-metal electrocatalysts;Nature Reviews Materials;2024-07-31

4. Multi-metal porous crystalline materials for electrocatalysis applications;Chinese Chemical Letters;2024-05

5. Navigating the unknown with AI: multiobjective Bayesian optimization of non-noble acidic OER catalysts;Journal of Materials Chemistry A;2024